Asymptotic Spectral Mapping for Hyperspectral Image Fusion

The fusion of low-resolution hyperspectral images (LR HSI) and high-resolution multispectral images (HR MSI) is a crucial approach for generating hyperspectral images (HSI). However, existing hyperspectral image fusion methods often rely on a single feature mapping process, which makes it difficult to accommodate the significant differences in features between the source images and the real images. Consequently, the generated images frequently exhibit varying degrees of information loss across different spectral bands and limit the overall performance of the fusion. To address this issue, we propose a novel hyperspectral image fusion network. Specifically, an asymptotic spectral mapping module is designed to enhance the detail information fitting capabilities of the fusion network. This module transforms the fitting process of missing information into multiple sets of fitting processes with varying degrees, which can map features with different spectral fidelity to various scales and gradually fit spectral information, thereby reducing spectral distortion. Additionally, we introduce an adaptive defect optimization loss that guides the network to focus on reconstructing regions with substantial spectral differences between LR HSI and HSI, optimizing the network’s constraints regarding the similarity between predicted and real images. Experimental results demonstrate that the proposed fusion network outperforms existing state-of-the-art methods across diverse datasets.